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Research Article | Open Access

Predicting EHL film thickness parameters by machine learning approaches

Max MARIAN1( ), Jonas MURSAK2, Marcel BARTZ2, Francisco J. PROFITO3, Andreas ROSENKRANZ4, Sandro WARTZACK2
Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago 6904411, Chile
Engineering Design, Friedrich-Alexander-University Erlangen-Nuremberg (FAU), Erlangen 91058, Germany
Department of Mechanical Engineering, Polytechnic School of the University of São Paulo, São Paulo 05508-030, Brazil
Department of Chemical Engineering, Biotechnology and Materials (DIQBM), FCFM, Universidad de Chile, Santiago 8370456, Chile
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Graphical Abstract

Abstract

Non-dimensional similarity groups and analytically solvable proximity equations can be used to estimate integral fluid film parameters of elastohydrodynamically lubricated (EHL) contacts. In this contribution, we demonstrate that machine learning (ML) and artificial intelligence (AI) approaches (support vector machines, Gaussian process regressions, and artificial neural networks) can predict relevant film parameters more efficiently and with higher accuracy and flexibility compared to sophisticated EHL simulations and analytically solvable proximity equations, respectively. For this purpose, we use data from EHL simulations based upon the full-system finite element (FE) solution and a Latin hypercube sampling. We verify that the original input data are required to train ML approaches to achieve coefficients of determination above 0.99. It is revealed that the architecture of artificial neural networks (neurons per layer and number of hidden layers) and activation functions influence the prediction accuracy. The impact of the number of training data is exemplified, and recommendations for a minimum database size are given. We ultimately demonstrate that artificial neural networks can predict the locally-resolved film thickness values over the contact domain 25-times faster than FE-based EHL simulations (R² values above 0.999). We assume that this will boost the use of ML approaches to predict EHL parameters and traction losses in multibody system dynamics simulations.

Keywords

machine learningelastohydrodynamic lubricationfilm thicknesssupport vector machineGaussian process regressionartificial neural network

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Friction
Volume 11 Issue 6,
June 2023
Pages 992-1013
DOI: 10.1007/s40544-022-0641-6
Cite this article:
MARIAN M, MURSAK J, BARTZ M, et al. Predicting EHL film thickness parameters by machine learning approaches. Friction, 2023, 11(6): 992-1013. https://doi.org/10.1007/s40544-022-0641-6

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Received: 24 November 2021
Revised: 09 March 2022
Accepted: 21 April 2022
Published: 12 June 2022
© The author(s) 2022.

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